year variable
1 2018 hurs, pr, rsds, sfcWind, tas, tasmax, tasmin, tz
2 2019 pr, rsds, tas, tasmax, tasmin, tz
3 2020 pr, rsds, tas, tasmax, tasmin, tz
4 2021 rsds, tas, tasmax, tasmin, tz
5 2023 clt, hurs, prec, ps, rsds, sfcWind, tas, tasmax, tasmin, tz
Total Cloud Cover Percentage(clt) percent Total cloud area fraction (reported as a percentage) for the whole atmospheric column as seen from the surface or the top of the atmosphere. Includes both large-scale and convective cloud.
Near-Surface Relative Humidity(hurs) percent The relative humidity with respect to liquid water for T> 0 C and with respect to ice for T
Precipitation(pr) kg m-2 day-1
includes both liquid and solid phases
Surface Air Pressure(ps) hPa surface pressure (not mean sea-level pressure)
Surface Downwelling Shortwave Flux in Air(rsds) W m-2
Surface solar irradiance for UV calculations.
tas_1 <-getChelsa("tas", dataset="CHELSA-daily",extent =c(124, 132, 33, 39),startdate =as.Date("2020-01-01"),enddate =as.Date("2020-01-02"))plot(pr_1, col =rev(terrain.colors(10)), main ="Precipitation")
코드 보기
plot(tas_1, col =rev(heat.colors(10)), main ="Average Temperature")
코드 보기
# 1. 디렉토리 설정save_path <-"Chelsa"if (!dir.exists(save_path)) dir.create(save_path, recursive =TRUE)# 2. 병렬 처리 계획 (워커 3개 유지)plan(mirai_multisession, workers =3)# 3. 분석 환경 설정target_year <-2020variables <-c("pr", "rsds", "tas", "tasmax", "tasmin")extent_val <-c(124, 132, 33, 39) # 한국 주변 영역# 4. 월별 병렬 루프results <-future_lapply(1:12, function(m) {# 날짜 계산 start_dt <-as.Date(sprintf("%d-%02d-01", target_year, m)) end_dt <-ceiling_date(start_dt, "month") -days(1) dates <-seq(start_dt, end_dt, by ="day") n_days <-length(dates)# 파일명 및 전체 경로 설정 file_nm <-sprintf("CHELSA_%d_%02d.nc", target_year, m) full_path <-file.path(save_path, file_nm)tryCatch({ monthly_data_list <-list()for (v in variables) {# 데이터 다운로드 시도 ds <-try(getChelsa(v, dataset ="CHELSA-daily", extent = extent_val,startdate = start_dt, enddate = end_dt), silent =TRUE)# 성공 여부 확인 및 데이터 처리if (!inherits(ds, "try-error") &&is(ds, "SpatRaster")) {names(ds) <-rep(v, nlyr(ds))time(ds) <- dates monthly_data_list[[v]] <- ds } else {# 실패 시 NA 래스터 생성 (공간 구조 유지) na_template <-rast(xmin=extent_val[1], xmax=extent_val[2], ymin=extent_val[3], ymax=extent_val[4], res=0.008333333, crs="EPSG:4326")# 일수(n_days)만큼 레이어 생성 후 시간 정보 주입 na_stack <-rast(lapply(1:n_days, function(x) na_template))names(na_stack) <-rep(v, n_days)time(na_stack) <- dates monthly_data_list[[v]] <- na_stack } }# 변수들을 하나의 SpatDataSet으로 결합 combined_sds <-sds(monthly_data_list)# netCDF 파일 쓰기 (최고 압축 적용)writeCDF(combined_sds, filename = full_path, zname ="time", compression =9, overwrite =TRUE)# 메모리 해제rm(monthly_data_list, combined_sds)gc()return(paste("Success:", full_path)) }, error =function(e) {return(paste("Error in month", m, ":", e$message)) })}, future.seed =TRUE)
